


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__UpperCamelCase     		=							logging.get_logger(__name__)


if is_vision_available():
						import PIL







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Any 			=		['pixel_values']




			def __init__(				self   ,    snake_case = True   ,    snake_case = None   ,    snake_case = PILImageResampling.BICUBIC   ,    snake_case = True   ,    snake_case = None   ,    snake_case = True   ,    snake_case = 1 / 255   ,    snake_case = True   ,    snake_case = None   ,    snake_case = None   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						super().__init__(**snake_case  )
						UpperCamelCase__       =							size if size is not None else {"shortest_edge": 224}
						UpperCamelCase__       =							get_size_dict(snake_case   ,    default_to_square=snake_case  )
						UpperCamelCase__       =							crop_size if crop_size is not None else {"height": 224, "width": 224}
						UpperCamelCase__       =							get_size_dict(snake_case   ,    default_to_square=snake_case   ,    param_name="crop_size"  )

						UpperCamelCase__       =							do_resize
						UpperCamelCase__       =							size
						UpperCamelCase__       =							resample
						UpperCamelCase__       =							do_center_crop
						UpperCamelCase__       =							crop_size
						UpperCamelCase__       =							do_rescale
						UpperCamelCase__       =							rescale_factor
						UpperCamelCase__       =							do_normalize
						UpperCamelCase__       =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						UpperCamelCase__       =							image_std if image_std is not None else OPENAI_CLIP_STD
						UpperCamelCase__       =							do_convert_rgb




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case = PILImageResampling.BICUBIC   ,    snake_case = None   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							get_size_dict(snake_case   ,    default_to_square=snake_case  )
						if "shortest_edge" not in size:
									raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
						UpperCamelCase__       =							get_resize_output_image_size(snake_case   ,    size=size["shortest_edge"]   ,    default_to_square=snake_case  )
						return resize(snake_case   ,    size=snake_case   ,    resample=snake_case   ,    data_format=snake_case   ,    **snake_case  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case = None   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							get_size_dict(snake_case  )
						if "height" not in size or "width" not in size:
									raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''  )
						return center_crop(snake_case   ,    size=(size["height"], size["width"])   ,    data_format=snake_case   ,    **snake_case  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case = None   ,    **snake_case   ,    ):

						'''simple docstring'''


						return rescale(snake_case   ,    scale=snake_case   ,    data_format=snake_case   ,    **snake_case  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case = None   ,    **snake_case   ,    ):

						'''simple docstring'''


						return normalize(snake_case   ,    mean=snake_case   ,    std=snake_case   ,    data_format=snake_case   ,    **snake_case  )







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = None   ,    snake_case = None   ,    snake_case = None   ,    snake_case = None   ,    snake_case = None   ,    snake_case = None   ,    snake_case = None   ,    snake_case = None   ,    snake_case = None   ,    snake_case = None   ,    snake_case = None   ,    snake_case = ChannelDimension.FIRST   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							do_resize if do_resize is not None else self.do_resize
						UpperCamelCase__       =							size if size is not None else self.size
						UpperCamelCase__       =							get_size_dict(snake_case   ,    param_name="size"   ,    default_to_square=snake_case  )
						UpperCamelCase__       =							resample if resample is not None else self.resample
						UpperCamelCase__       =							do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCamelCase__       =							crop_size if crop_size is not None else self.crop_size
						UpperCamelCase__       =							get_size_dict(snake_case   ,    param_name="crop_size"   ,    default_to_square=snake_case  )
						UpperCamelCase__       =							do_rescale if do_rescale is not None else self.do_rescale
						UpperCamelCase__       =							rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCamelCase__       =							do_normalize if do_normalize is not None else self.do_normalize
						UpperCamelCase__       =							image_mean if image_mean is not None else self.image_mean
						UpperCamelCase__       =							image_std if image_std is not None else self.image_std
						UpperCamelCase__       =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						UpperCamelCase__       =							make_list_of_images(snake_case  )

						if not valid_images(snake_case  ):
									raise ValueError(
									    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
									    "torch.Tensor, tf.Tensor or jax.ndarray."  )

						if do_resize and size is None:
									raise ValueError("Size must be specified if do_resize is True."  )

						if do_center_crop and crop_size is None:
									raise ValueError("Crop size must be specified if do_center_crop is True."  )

						if do_rescale and rescale_factor is None:
									raise ValueError("Rescale factor must be specified if do_rescale is True."  )

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("Image mean and std must be specified if do_normalize is True."  )

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
									UpperCamelCase__       =							[convert_to_rgb(snake_case  ) for image in images]

						# All transformations expect numpy arrays.
						UpperCamelCase__       =							[to_numpy_array(snake_case  ) for image in images]

						if do_resize:
									UpperCamelCase__       =							[self.resize(image=snake_case   ,    size=snake_case   ,    resample=snake_case  ) for image in images]

						if do_center_crop:
									UpperCamelCase__       =							[self.center_crop(image=snake_case   ,    size=snake_case  ) for image in images]

						if do_rescale:
									UpperCamelCase__       =							[self.rescale(image=snake_case   ,    scale=snake_case  ) for image in images]

						if do_normalize:
									UpperCamelCase__       =							[self.normalize(image=snake_case   ,    mean=snake_case   ,    std=snake_case  ) for image in images]

						UpperCamelCase__       =							[to_channel_dimension_format(snake_case   ,    snake_case  ) for image in images]

						UpperCamelCase__       =							{"pixel_values": images}
						return BatchFeature(data=snake_case   ,    tensor_type=snake_case  )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase     		=							{
    'configuration_xlm_roberta_xl': [
        'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaXLConfig',
        'XLMRobertaXLOnnxConfig',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase     		=							[
						    'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'XLMRobertaXLForCausalLM',
						    'XLMRobertaXLForMaskedLM',
						    'XLMRobertaXLForMultipleChoice',
						    'XLMRobertaXLForQuestionAnswering',
						    'XLMRobertaXLForSequenceClassification',
						    'XLMRobertaXLForTokenClassification',
						    'XLMRobertaXLModel',
						    'XLMRobertaXLPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    XLMRobertaXLConfig,
						    XLMRobertaXLOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_roberta_xl import (
												    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMRobertaXLForCausalLM,
												    XLMRobertaXLForMaskedLM,
												    XLMRobertaXLForMultipleChoice,
												    XLMRobertaXLForQuestionAnswering,
												    XLMRobertaXLForSequenceClassification,
												    XLMRobertaXLForTokenClassification,
												    XLMRobertaXLModel,
												    XLMRobertaXLPreTrainedModel,
												)

else:
						import sys

						__UpperCamelCase     		=							_LazyModule(__name__, globals()['__file__'], _import_structure)


